1 Multi-Dimensional Signal Processing & Pattern Recognition 2012 Fall Semester International Course Lecture Nozomu HAMADA 浜田 望
1
Multi-Dimensional Signal Processing &
Pattern Recognition
2012 Fall Semester International Course Lecture
Nozomu HAMADA 浜田 望
Course Introduction
• Course Plan (Calendar)
• Lecture Note, Textbook, Grades, etc.
• Course Overview
Part 1 : Bayesian Signal Processing
Part 2 : Pattern Recognition
Machine learning
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Course Calendar Class DATE Contents
1 Sep. 26 Course information & Course overview
2 Oct. 4 Bayes Estimation
3 〃 11 Classical Bayes Estimation - Kalman Filter -
4 〃 18 Simulation-based Bayesian Methods
5 〃 25 Modern Bayesian Estimation Particle Filter
6 Nov. 1 HMM(Hidden Markov Model)
Nov. 8 No Class
7 〃 15 Supervised Learning
8 〃 29 Bayesian Decision
9 Dec. 6 PCA(Principal Component Analysis)
10 〃 13 ICA(Independent Component Analysis)
11 〃 20 Applications of PCA and ICA
12 〃 27 Clustering, k-means et al.
13 Jan. 17 Other Topics 1 Kernel machine.
14 〃 22(Tue) Other Topics 2
4
Prerequisites
Elementary of Discrete-time signals and Systems
Elementary of Probability/statistics and Matrix Theory
References:
1) J. V. Candy “Bayesian Signal Processing” Wiley 2009
2) B.Ristic, et. al. “Beyond the Kalman Filter”, Artech house 2004
3) R.O. Duda, P.E. Hart, and D. G. Stork, “Pattern Classification”,
John Wiley & Sons, 2nd edition, 2004
4) C. M. Bishop, “Pattern Recognition and Machine Learning”,
Springer, 2006
5) E. Alpaydin, Introduction to Machine Learning, MIT Press, 2009
6) A. Huvarinen et. al., ”Independent Component Analysis”
Wiley-Interscience 2001
Japanese textbooks
Japanese translations of 3),4),6)
片山徹、「非線形カルマンフィルタ」 朝倉書店 2011
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E. Alpaydin,
Introduction to
Machine Learning,
MIT Press, 2009
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■Grading:
Homeworks
Report at the end of the term (Jan. 2013)
- Discuss some subjects -
■Lecture Note
All lecture slides and handouts are available at the
keio jp. web site “class support”.
■Office
Room 25-418
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Part 1 : Bayesian Signal Processing
- Bayesian Estimation, Kalman Filter, Monte Carlo,Particle Filter -
Part 2: Pattern Recognition
- Bayesian Decision,PCA & ICA, Clustering, Karnel Methods -
Object Tracking Problem
13
Handwritten
Digit
Recognition
15
Face recognition problem
Given a training database
of facial photographs with
identification tags on that.
Design an automated system
to recognize the identity of a
new image
of the person